/* ================================================================ Feature Engineering Explorer — Core JS Logic ================================================================ */ /***** ---------- 1. DATA ------------------------------------------------ */ const datasets = { missingAge: [25, null, 30, 35, null, 40, 45], studentHeights: [150, 155, 160, 165, 170, 175, 180, 185, 260], colors: ['Red', 'Green', 'Blue', 'Red', 'Green', 'Yellow'], houseData: { sqft: [900, 1000, 1200, 1500, 1800, 2000], bedrooms: [2, 2, 3, 3, 4, 4] } }; /***** ---------- 2. NAVIGATION & ACTIVE LINK --------------------------- */ const navLinks = document.querySelectorAll('.nav__link'); navLinks.forEach((link) => { link.addEventListener('click', () => { document.querySelector('.nav__link.active')?.classList.remove('active'); link.classList.add('active'); }); }); // Highlight section in view const observer = new IntersectionObserver( (entries) => { entries.forEach((entry) => { if (entry.isIntersecting) { const id = entry.target.id; document.querySelector('.nav__link.active')?.classList.remove('active'); document .querySelector(`.nav__link[href="#${id}"]`) .classList.add('active'); } }); }, { rootMargin: '-40% 0px -55% 0px' } ); document.querySelectorAll('.topic-section').forEach((sec) => observer.observe(sec)); /***** ---------- 3. CANVAS HELPERS ------------------------------------ */ function clearCanvas(ctx) { ctx.clearRect(0, 0, ctx.canvas.width, ctx.canvas.height); } function drawBar(ctx, x, y, w, h, color) { ctx.fillStyle = color; ctx.fillRect(x, y, w, h); } function drawAxis(ctx) { ctx.strokeStyle = 'rgba(255,255,255,0.2)'; ctx.lineWidth = 1; ctx.beginPath(); ctx.moveTo(40, 10); ctx.lineTo(40, ctx.canvas.height - 30); ctx.lineTo(ctx.canvas.width - 10, ctx.canvas.height - 30); ctx.stroke(); } /***** ---------- 4. INTRO CANVAS -------------------------------------- */ (function introVis() { const c = document.getElementById('canvas-intro'); if (!c) return; const ctx = c.getContext('2d'); function draw() { clearCanvas(ctx); const steps = [ 'Raw Data', 'Cleaning', 'Feature Engineering', 'Model', 'Prediction' ]; const boxW = 110, boxH = 40, gap = 20, startX = 30, y = 100; ctx.font = '14px Inter, sans-serif'; ctx.textAlign = 'center'; ctx.textBaseline = 'middle'; steps.forEach((step, i) => { const x = startX + i * (boxW + gap); // box ctx.fillStyle = i === 2 ? '#00d4ff' : '#252a47'; ctx.strokeStyle = '#00d4ff'; ctx.lineWidth = 2; ctx.fillRect(x, y, boxW, boxH); ctx.strokeRect(x, y, boxW, boxH); // text ctx.fillStyle = '#e0e6ed'; ctx.fillText(step, x + boxW / 2, y + boxH / 2); // arrow if (i < steps.length - 1) { ctx.strokeStyle = '#e0e6ed'; ctx.beginPath(); ctx.moveTo(x + boxW, y + boxH / 2); ctx.lineTo(x + boxW + gap - 10, y + boxH / 2); ctx.lineTo(x + boxW + gap - 14, y + boxH / 2 - 4); ctx.moveTo(x + boxW + gap - 10, y + boxH / 2); ctx.lineTo(x + boxW + gap - 14, y + boxH / 2 + 4); ctx.stroke(); } }); } draw(); })(); /***** ---------- 5. MISSING DATA CANVAS ------------------------------- */ (function missingDataVis() { const c = document.getElementById('canvas-missing-data'); if (!c) return; const ctx = c.getContext('2d'); const data = [25, null, 30, 35, null, 40, 45]; function impute(data, method) { const arr = [...data]; const observed = arr.filter((v) => v != null); switch (method) { case 'mean': { const mean = observed.reduce((a, b) => a + b, 0) / observed.length; return arr.map((v) => (v == null ? mean : v)); } case 'median': { const sorted = observed.slice().sort((a, b) => a - b); const mid = Math.floor(sorted.length / 2); const median = sorted.length % 2 !== 0 ? sorted[mid] : (sorted[mid - 1] + sorted[mid]) / 2; return arr.map((v) => (v == null ? median : v)); } case 'mode': { const freq = {}; observed.forEach((v) => (freq[v] = (freq[v] || 0) + 1)); let mode = observed[0]; let max = 0; for (const key in freq) { if (freq[key] > max) { max = freq[key]; mode = Number(key); } } return arr.map((v) => (v == null ? mode : v)); } case 'ffill': { let last = observed[0]; return arr.map((v) => { if (v == null) return last; last = v; return v; }); } case 'knn': { // very naive KNN with k = 3 using nearest neighbors by index const k = 3; return arr.map((v, idx) => { if (v != null) return v; const neighbors = []; // look left and right until we gather k neighbors let left = idx - 1, right = idx + 1; while (neighbors.length < k && (left >= 0 || right < arr.length)) { if (left >= 0 && arr[left] != null) neighbors.push(arr[left]); if (neighbors.length === k) break; if (right < arr.length && arr[right] != null) neighbors.push(arr[right]); left--; right++; } if (neighbors.length === 0) return 0; return neighbors.reduce((a, b) => a + b, 0) / neighbors.length; }); } default: return arr; } } function draw(method) { const original = [...data]; const imputed = impute(data, method); clearCanvas(ctx); // Title and description ctx.fillStyle = '#00d4ff'; ctx.font = '16px Inter, sans-serif'; let methodName = ''; if (method === 'mean') methodName = 'Mean Imputation: Missing values filled with 35'; else if (method === 'median') methodName = 'Median Imputation: Missing values filled with 35'; else if (method === 'knn') methodName = 'KNN Imputation: Using k=2 neighbors'; ctx.fillText(methodName, 40, 30); // Draw axis ctx.strokeStyle = 'rgba(255,255,255,0.2)'; ctx.lineWidth = 1; ctx.beginPath(); ctx.moveTo(60, 60); ctx.lineTo(60, 420); ctx.lineTo(760, 420); ctx.stroke(); const maxVal = 50; const barW = 70; const gap = 20; const chartBottom = 420; imputed.forEach((val, i) => { const x = 80 + i * (barW + gap); const h = (val / maxVal) * 320; const y = chartBottom - h; const origMissing = original[i] == null; let color = '#00d4ff'; if (origMissing && method === 'mean') color = '#00ffff'; else if (origMissing && method === 'median') color = '#ff6b35'; else if (origMissing && method === 'knn') color = '#00ff88'; else if (origMissing) color = '#888'; drawBar(ctx, x, y, barW, h, color); // Value label ctx.fillStyle = '#e0e6ed'; ctx.font = '14px Inter, sans-serif'; ctx.textAlign = 'center'; ctx.fillText(Math.round(val), x + barW/2, y - 10); // Missing marker if (original[i] == null) { ctx.fillStyle = '#ff6b35'; ctx.font = '20px Inter, sans-serif'; ctx.fillText('?', x + barW/2, chartBottom + 30); } }); ctx.textAlign = 'left'; // Legend ctx.font = '12px Inter, sans-serif'; ctx.fillStyle = '#00d4ff'; ctx.fillText('■ Original values', 80, 470); ctx.fillStyle = method === 'mean' ? '#00ffff' : method === 'median' ? '#ff6b35' : '#00ff88'; ctx.fillText('■ Imputed values', 220, 470); } // Button event listeners const btnMean = document.getElementById('btn-mean-impute'); const btnMedian = document.getElementById('btn-median-impute'); const btnKnn = document.getElementById('btn-knn-impute'); if (btnMean) btnMean.addEventListener('click', () => draw('mean')); if (btnMedian) btnMedian.addEventListener('click', () => draw('median')); if (btnKnn) btnKnn.addEventListener('click', () => draw('knn')); // Initial draw draw('mean'); })(); /***** ---------- 6. OUTLIER CANVAS ------------------------------------ */ (function outlierVis() { const c = document.getElementById('canvas-outliers'); if (!c) return; const ctx = c.getContext('2d'); const data = [150, 155, 160, 165, 170, 175, 180, 185, 260]; function draw(method) { const mean = data.reduce((a, b) => a + b) / data.length; const std = Math.sqrt( data.map((v) => (v - mean) ** 2).reduce((a, b) => a + b) / data.length ); const sorted = [...data].sort((a, b) => a - b); const q1 = 157.5; const q3 = 182.5; const iqr = 25; const lowerBound = q1 - 1.5 * iqr; const upperBound = q3 + 1.5 * iqr; clearCanvas(ctx); // Title ctx.fillStyle = '#00d4ff'; ctx.font = '16px Inter, sans-serif'; let title = ''; if (method === 'iqr') title = 'IQR Method: 1 outlier detected (260cm)'; else if (method === 'zscore') title = 'Z-Score: 1 outlier detected'; else if (method === 'winsorize') title = 'Winsorization: Capped at 95th percentile (185cm)'; ctx.fillText(title, 40, 30); // Draw axis ctx.strokeStyle = 'rgba(255,255,255,0.2)'; ctx.lineWidth = 1; ctx.beginPath(); ctx.moveTo(60, 60); ctx.lineTo(60, 420); ctx.lineTo(760, 420); ctx.stroke(); const maxVal = 280; const plotBottom = 420; // Draw scatter plot data.forEach((v, i) => { const x = 100 + i * 70; const y = plotBottom - (v / maxVal) * 320; let isOutlier = false; if (method === 'iqr') { isOutlier = v < lowerBound || v > upperBound; } else if (method === 'zscore') { const z = Math.abs((v - mean) / std); isOutlier = z > 3; } else if (method === 'winsorize') { isOutlier = v > 185; } ctx.fillStyle = isOutlier ? '#ff6b35' : '#00d4ff'; ctx.beginPath(); ctx.arc(x, y, 8, 0, Math.PI * 2); ctx.fill(); // Value label ctx.fillStyle = '#e0e6ed'; ctx.font = '12px Inter, sans-serif'; ctx.textAlign = 'center'; ctx.fillText(v, x, y - 15); }); ctx.textAlign = 'left'; // Draw box plot if IQR method if (method === 'iqr') { const boxY = 100; const boxX1 = 100; const boxX2 = 660; const q1X = boxX1 + (q1 / maxVal) * (boxX2 - boxX1); const q3X = boxX1 + (q3 / maxVal) * (boxX2 - boxX1); const medX = boxX1 + (170 / maxVal) * (boxX2 - boxX1); // Box ctx.strokeStyle = '#00ffff'; ctx.lineWidth = 3; ctx.strokeRect(q1X, boxY - 15, q3X - q1X, 30); // Median line ctx.beginPath(); ctx.moveTo(medX, boxY - 15); ctx.lineTo(medX, boxY + 15); ctx.stroke(); // Bounds lines ctx.strokeStyle = '#ff6b35'; ctx.setLineDash([5, 5]); const ubX = boxX1 + (upperBound / maxVal) * (boxX2 - boxX1); ctx.beginPath(); ctx.moveTo(ubX, 60); ctx.lineTo(ubX, 420); ctx.stroke(); ctx.setLineDash([]); // Labels ctx.fillStyle = '#e0e6ed'; ctx.font = '11px Inter, sans-serif'; ctx.fillText(`Q1=${q1}`, q1X - 25, boxY + 35); ctx.fillText(`Q3=${q3}`, q3X - 25, boxY + 35); ctx.fillStyle = '#ff6b35'; ctx.fillText(`UB=${upperBound}`, ubX - 30, 50); } // Mean line for zscore if (method === 'zscore') { ctx.strokeStyle = '#00ffff'; const meanY = plotBottom - (mean / maxVal) * 320; ctx.lineWidth = 2; ctx.beginPath(); ctx.moveTo(60, meanY); ctx.lineTo(760, meanY); ctx.stroke(); ctx.fillStyle = '#00ffff'; ctx.fillText(`Mean = ${mean.toFixed(1)}`, 680, meanY - 10); } // Legend ctx.fillStyle = '#00d4ff'; ctx.fillText('● Normal', 80, 470); ctx.fillStyle = '#ff6b35'; ctx.fillText('● Outlier', 180, 470); } // Button event listeners const btnIqr = document.getElementById('btn-detect-iqr'); const btnZscore = document.getElementById('btn-detect-zscore'); const btnWinsorize = document.getElementById('btn-winsorize'); if (btnIqr) btnIqr.addEventListener('click', () => draw('iqr')); if (btnZscore) btnZscore.addEventListener('click', () => draw('zscore')); if (btnWinsorize) btnWinsorize.addEventListener('click', () => draw('winsorize')); draw('iqr'); })(); /***** ---------- 7. SCALING CANVAS (Simplified) ----------------------- */ (function scalingVis() { const c = document.getElementById('canvas-scaling'); if (!c) return; const ctx = c.getContext('2d'); const rawX = [160, 165, 170, 175, 180]; const rawY = [60, 65, 70, 75, 80]; function scale(values, method) { if (method === 'minmax') { const min = Math.min(...values); const max = Math.max(...values); return values.map((v) => (v - min) / (max - min)); } if (method === 'zscore') { const mean = values.reduce((a, b) => a + b) / values.length; const std = Math.sqrt(values.map((v) => (v - mean) ** 2).reduce((a, b) => a + b) / values.length); return values.map((v) => (v - mean) / std); } if (method === 'robust') { const sorted = values.slice().sort((a, b) => a - b); const q1 = sorted[Math.floor(sorted.length * 0.25)]; const q3 = sorted[Math.floor(sorted.length * 0.75)]; const iqr = q3 - q1; const med = sorted[Math.floor(sorted.length / 2)]; return values.map((v) => (v - med) / iqr); } return values; } function draw(method) { clearCanvas(ctx); // Title ctx.fillStyle = '#00d4ff'; ctx.font = '16px Inter, sans-serif'; let title = ''; if (method === 'minmax') title = 'Min-Max: Data scaled to [0, 1]'; else if (method === 'zscore') title = 'Standardization: Mean=0, Std=1'; else if (method === 'robust') title = 'Robust Scaler: Less sensitive to outliers'; ctx.fillText(title, 40, 30); const scaledX = scale(rawX, method); const scaledY = scale(rawY, method); // Left side: Original data ctx.fillStyle = '#e0e6ed'; ctx.font = '14px Inter, sans-serif'; ctx.fillText('Original Data', 100, 80); ctx.strokeStyle = 'rgba(255,255,255,0.2)'; ctx.strokeRect(50, 100, 300, 300); rawX.forEach((xVal, i) => { const yVal = rawY[i]; const x = 50 + ((xVal - 155) / 30) * 300; const y = 400 - ((yVal - 55) / 30) * 300; ctx.fillStyle = '#00d4ff'; ctx.beginPath(); ctx.arc(x, y, 6, 0, Math.PI * 2); ctx.fill(); }); // Axis labels ctx.fillStyle = '#a8b2c1'; ctx.font = '11px Inter, sans-serif'; ctx.fillText('Height', 160, 425); ctx.save(); ctx.translate(30, 250); ctx.rotate(-Math.PI / 2); ctx.fillText('Weight', 0, 0); ctx.restore(); // Right side: Scaled data ctx.fillStyle = '#e0e6ed'; ctx.font = '14px Inter, sans-serif'; ctx.fillText('Scaled Data', 500, 80); ctx.strokeStyle = 'rgba(255,255,255,0.2)'; ctx.strokeRect(450, 100, 300, 300); const minScaledX = Math.min(...scaledX); const maxScaledX = Math.max(...scaledX); const minScaledY = Math.min(...scaledY); const maxScaledY = Math.max(...scaledY); const rangeX = maxScaledX - minScaledX || 1; const rangeY = maxScaledY - minScaledY || 1; scaledX.forEach((xVal, i) => { const yVal = scaledY[i]; const x = 450 + ((xVal - minScaledX) / rangeX) * 300; const y = 400 - ((yVal - minScaledY) / rangeY) * 300; ctx.fillStyle = '#00ffff'; ctx.beginPath(); ctx.arc(x, y, 6, 0, Math.PI * 2); ctx.fill(); }); // Stats ctx.fillStyle = '#00ffff'; ctx.font = '11px Inter, sans-serif'; if (method === 'minmax') { ctx.fillText('Range: [0, 1]', 460, 425); } else if (method === 'zscore') { ctx.fillText('Mean: 0, Std: 1', 460, 425); } else if (method === 'robust') { ctx.fillText('Median centered', 460, 425); } } // Button event listeners const btnMinmax = document.getElementById('btn-minmax'); const btnStd = document.getElementById('btn-standardize'); const btnRobust = document.getElementById('btn-robust'); if (btnMinmax) btnMinmax.addEventListener('click', () => draw('minmax')); if (btnStd) btnStd.addEventListener('click', () => draw('zscore')); if (btnRobust) btnRobust.addEventListener('click', () => draw('robust')); draw('minmax'); })(); /***** ---------- 8. ENCODING CANVAS ---------------------------------- */ (function encodingVis() { const c = document.getElementById('canvas-encoding'); if (!c) return; const ctx = c.getContext('2d'); const categories = ['Red', 'Green', 'Blue', 'Red', 'Green', 'Yellow']; function encode(data, method) { if (method === 'label') { const unique = [...new Set(data)]; return data.map(v => unique.indexOf(v)); } else if (method === 'onehot') { const unique = [...new Set(data)]; return data.map(v => { const arr = new Array(unique.length).fill(0); arr[unique.indexOf(v)] = 1; return arr; }); } else if (method === 'target') { const targetMean = { 'Red': 0.8, 'Green': 0.5, 'Blue': 0.3, 'Yellow': 0.6 }; return data.map(v => targetMean[v] || 0); } return []; } function draw(method) { const encoded = encode(categories, method); clearCanvas(ctx); // Title ctx.fillStyle = '#00d4ff'; ctx.font = '16px Inter, sans-serif'; let title = ''; if (method === 'label') title = 'Label Encoding: Categories → Integers'; else if (method === 'onehot') title = 'One-Hot: 4 categorical → 4 binary columns'; else if (method === 'target') title = 'Target Encoding: Category → Mean(target)'; ctx.fillText(title, 40, 30); ctx.fillStyle = '#e0e6ed'; ctx.font = '14px Inter, sans-serif'; ctx.fillText('Original', 120, 80); ctx.fillText('Encoded', 420, 80); const colors = { 'Red': '#ff6b35', 'Green': '#00ff88', 'Blue': '#00d4ff', 'Yellow': '#ffcc00' }; categories.forEach((cat, i) => { const y = 120 + i * 50; // Original ctx.fillStyle = colors[cat]; ctx.fillRect(80, y, 100, 35); ctx.fillStyle = '#000'; ctx.font = '14px Inter, sans-serif'; ctx.textAlign = 'center'; ctx.fillText(cat, 130, y + 22); // Arrow ctx.fillStyle = '#e0e6ed'; ctx.font = '18px Inter, sans-serif'; ctx.fillText('→', 240, y + 22); // Encoded ctx.fillStyle = '#00ffff'; ctx.font = '14px Inter, sans-serif'; let encodedText = ''; if (method === 'onehot') { encodedText = '[' + encoded[i].join(', ') + ']'; } else { encodedText = String(encoded[i]); } ctx.fillText(encodedText, 450, y + 22); }); ctx.textAlign = 'left'; // Mapping legend if (method === 'label') { const unique = [...new Set(categories)]; ctx.fillStyle = '#e0e6ed'; ctx.font = '12px Inter, sans-serif'; ctx.fillText('Mapping:', 80, 450); unique.forEach((cat, i) => { ctx.fillStyle = colors[cat]; ctx.fillText(`${cat} = ${i}`, 180 + i * 80, 450); }); } } // Button event listeners const btnLabel = document.getElementById('btn-label-encode'); const btnOnehot = document.getElementById('btn-onehot-encode'); const btnTarget = document.getElementById('btn-target-encode'); if (btnLabel) btnLabel.addEventListener('click', () => draw('label')); if (btnOnehot) btnOnehot.addEventListener('click', () => draw('onehot')); if (btnTarget) btnTarget.addEventListener('click', () => draw('target')); draw('label'); })(); /***** ---------- 9. IMBALANCED DATA CANVAS ---------------------------- */ (function imbalancedVis() { const c = document.getElementById('canvas-imbalanced'); if (!c) return; const ctx = c.getContext('2d'); function draw(method) { clearCanvas(ctx); // Title ctx.fillStyle = '#00d4ff'; ctx.font = '16px Inter, sans-serif'; let title = ''; if (method === 'rus') title = 'RUS: 90 → 10 (lost data)'; else if (method === 'ros') title = 'ROS: 10 → 90 (by duplication)'; else if (method === 'smote') title = 'SMOTE: 10 → 90 (synthetic samples)'; ctx.fillText(title, 40, 30); const majorityCount = 90; const minorityCount = 10; let balancedMajority = majorityCount; let balancedMinority = minorityCount; if (method === 'rus') { balancedMajority = minorityCount; } else if (method === 'ros') { balancedMinority = majorityCount; } else if (method === 'smote') { balancedMinority = majorityCount; } ctx.fillStyle = '#e0e6ed'; ctx.font = '14px Inter, sans-serif'; ctx.fillText('Before', 180, 80); ctx.fillText('After', 540, 80); // Scatter plot visualization // Before - left side for (let i = 0; i < majorityCount; i++) { const x = 80 + Math.random() * 200; const y = 120 + Math.random() * 250; ctx.fillStyle = '#00d4ff'; ctx.beginPath(); ctx.arc(x, y, 3, 0, Math.PI * 2); ctx.fill(); } for (let i = 0; i < minorityCount; i++) { const x = 80 + Math.random() * 200; const y = 120 + Math.random() * 250; ctx.fillStyle = '#ff6b35'; ctx.beginPath(); ctx.arc(x, y, 3, 0, Math.PI * 2); ctx.fill(); } // After - right side for (let i = 0; i < balancedMajority; i++) { const x = 440 + Math.random() * 200; const y = 120 + Math.random() * 250; ctx.fillStyle = '#00d4ff'; ctx.beginPath(); ctx.arc(x, y, 3, 0, Math.PI * 2); ctx.fill(); } for (let i = 0; i < balancedMinority; i++) { const x = 440 + Math.random() * 200; const y = 120 + Math.random() * 250; ctx.fillStyle = method === 'smote' ? '#00ff88' : '#ff6b35'; ctx.beginPath(); ctx.arc(x, y, 3, 0, Math.PI * 2); ctx.fill(); } // Stats ctx.fillStyle = '#e0e6ed'; ctx.font = '12px Inter, sans-serif'; ctx.fillText(`Majority: ${majorityCount}`, 120, 400); ctx.fillText(`Minority: ${minorityCount}`, 120, 420); ctx.fillText(`Ratio: ${(majorityCount/minorityCount).toFixed(1)}:1`, 120, 440); ctx.fillText(`Majority: ${balancedMajority}`, 480, 400); ctx.fillText(`Minority: ${balancedMinority}`, 480, 420); ctx.fillText(`Ratio: ${(balancedMajority/balancedMinority).toFixed(1)}:1`, 480, 440); // Legend ctx.fillStyle = '#00d4ff'; ctx.fillText('● Majority class', 300, 470); ctx.fillStyle = method === 'smote' ? '#00ff88' : '#ff6b35'; ctx.fillText('● Minority class', 450, 470); } // Button event listeners const btnRus = document.getElementById('btn-rus'); const btnRos = document.getElementById('btn-ros'); const btnSmote = document.getElementById('btn-smote'); if (btnRus) btnRus.addEventListener('click', () => draw('rus')); if (btnRos) btnRos.addEventListener('click', () => draw('ros')); if (btnSmote) btnSmote.addEventListener('click', () => draw('smote')); draw('rus'); })(); /***** ---------- 9. FEATURE SELECTION CANVAS -------------------------- */ (function featureSelectionVis() { const c = document.getElementById('canvas-selection'); if (!c) return; const ctx = c.getContext('2d'); const allFeatures = ['sqft', 'bedrooms', 'age', 'location', 'garage']; const importance = [0.85, 0.62, 0.45, 0.73, 0.38]; function draw(method) { clearCanvas(ctx); // Title ctx.fillStyle = '#00d4ff'; ctx.font = '16px Inter, sans-serif'; let title = ''; let selected = []; if (method === 'backward') { title = 'Backward Elimination: 3 features selected'; selected = [0, 1, 3]; // sqft, bedrooms, location } else if (method === 'forward') { title = 'Forward Selection: Added 3 features'; selected = [0, 3, 1]; // sqft, location, bedrooms } else if (method === 'rfe') { title = 'RFE: Optimal 3 features'; selected = [0, 1, 3]; // sqft, bedrooms, location } ctx.fillText(title, 40, 30); // Feature importance bar chart ctx.fillStyle = '#e0e6ed'; ctx.font = '14px Inter, sans-serif'; ctx.fillText('Feature Importance', 80, 80); const barHeight = 40; const maxWidth = 500; allFeatures.forEach((feature, i) => { const y = 120 + i * (barHeight + 20); const barWidth = importance[i] * maxWidth; const isSelected = selected.includes(i); // Bar ctx.fillStyle = isSelected ? '#00ffff' : '#555'; ctx.fillRect(200, y, barWidth, barHeight); // Feature name ctx.fillStyle = isSelected ? '#00ffff' : '#a8b2c1'; ctx.font = '14px Inter, sans-serif'; ctx.textAlign = 'right'; ctx.fillText(feature, 180, y + 25); // Importance value ctx.textAlign = 'left'; ctx.fillText(importance[i].toFixed(2), barWidth + 210, y + 25); // Selected marker if (isSelected) { ctx.fillStyle = '#00ff88'; ctx.font = '16px Inter, sans-serif'; ctx.fillText('✓', 720, y + 25); } }); ctx.textAlign = 'left'; // Legend ctx.fillStyle = '#00ffff'; ctx.font = '12px Inter, sans-serif'; ctx.fillText('■ Selected features', 200, 460); ctx.fillStyle = '#555'; ctx.fillText('■ Removed features', 380, 460); } // Button event listeners const btnBackward = document.getElementById('btn-backward-elim'); const btnForward = document.getElementById('btn-forward-select'); const btnRfe = document.getElementById('btn-rfe'); if (btnBackward) btnBackward.addEventListener('click', () => draw('backward')); if (btnForward) btnForward.addEventListener('click', () => draw('forward')); if (btnRfe) btnRfe.addEventListener('click', () => draw('rfe')); draw('rfe'); })(); /***** ---------- 10. COMPREHENSIVE EDA CANVAS ------------------------- */ (function edaVis() { const featureSelect = document.getElementById('edaFeature'); const confidenceSlider = document.getElementById('confidenceLevel'); const confidenceSpan = document.getElementById('confidenceValue'); const calcBtn = document.getElementById('calculateStats'); const corrBtn = document.getElementById('showCorrelation'); const c = document.getElementById('canvas-eda'); if (!c) return; const ctx = c.getContext('2d'); // Sample EDA dataset const edaData = { age: [25, 30, 35, 40, 45, 50, 55, 60, 65, 70], income: [35000, 42000, 55000, 65000, 58000, 72000, 80000, 70000, 90000, 95000], credit: [650, 680, 720, 700, 690, 750, 780, 760, 800, 820] }; let showingCorrelation = false; function calculateStats(data) { const sorted = [...data].sort((a, b) => a - b); const n = data.length; const mean = data.reduce((a, b) => a + b) / n; const median = n % 2 === 0 ? (sorted[n/2-1] + sorted[n/2]) / 2 : sorted[Math.floor(n/2)]; const variance = data.reduce((sum, v) => sum + (v - mean) ** 2, 0) / n; const std = Math.sqrt(variance); const q1 = sorted[Math.floor(n * 0.25)]; const q3 = sorted[Math.floor(n * 0.75)]; const iqr = q3 - q1; const mode = data.sort((a,b) => data.filter(v => v === a).length - data.filter(v => v === b).length ).pop(); return { mean, median, mode, std, q1, q3, iqr, sorted, min: sorted[0], max: sorted[n-1] }; } function drawHistogram(data, stats, x, y, w, h) { // Create bins const numBins = 6; const binWidth = (stats.max - stats.min) / numBins; const bins = new Array(numBins).fill(0); data.forEach(val => { const binIndex = Math.min(Math.floor((val - stats.min) / binWidth), numBins - 1); bins[binIndex]++; }); const maxBin = Math.max(...bins); const barWidth = w / numBins; // Draw bars bins.forEach((count, i) => { const barH = (count / maxBin) * (h - 40); const barX = x + i * barWidth; const barY = y + h - 30 - barH; ctx.fillStyle = '#00d4ff'; ctx.fillRect(barX, barY, barWidth - 2, barH); }); // Draw mean line const meanX = x + ((stats.mean - stats.min) / (stats.max - stats.min)) * w; ctx.strokeStyle = '#00ffff'; ctx.lineWidth = 2; ctx.beginPath(); ctx.moveTo(meanX, y); ctx.lineTo(meanX, y + h - 30); ctx.stroke(); // Draw median line const medianX = x + ((stats.median - stats.min) / (stats.max - stats.min)) * w; ctx.strokeStyle = '#ff6b35'; ctx.beginPath(); ctx.moveTo(medianX, y); ctx.lineTo(medianX, y + h - 30); ctx.stroke(); // Labels ctx.fillStyle = '#e0e6ed'; ctx.font = '11px Inter, sans-serif'; ctx.fillText('Histogram', x, y - 5); } function drawBoxPlot(data, stats, x, y, w, h) { const scale = w / (stats.max - stats.min); const center = y + h / 2; const boxHeight = 40; // Whiskers const minX = x + (stats.min - stats.min) * scale; const maxX = x + (stats.max - stats.min) * scale; const q1X = x + (stats.q1 - stats.min) * scale; const q3X = x + (stats.q3 - stats.min) * scale; const medX = x + (stats.median - stats.min) * scale; ctx.strokeStyle = '#00d4ff'; ctx.lineWidth = 2; // Left whisker ctx.beginPath(); ctx.moveTo(minX, center); ctx.lineTo(q1X, center); ctx.stroke(); // Right whisker ctx.beginPath(); ctx.moveTo(q3X, center); ctx.lineTo(maxX, center); ctx.stroke(); // Box ctx.fillStyle = 'rgba(0, 212, 255, 0.3)'; ctx.fillRect(q1X, center - boxHeight/2, q3X - q1X, boxHeight); ctx.strokeStyle = '#00d4ff'; ctx.strokeRect(q1X, center - boxHeight/2, q3X - q1X, boxHeight); // Median line ctx.strokeStyle = '#ff6b35'; ctx.lineWidth = 3; ctx.beginPath(); ctx.moveTo(medX, center - boxHeight/2); ctx.lineTo(medX, center + boxHeight/2); ctx.stroke(); // Labels ctx.fillStyle = '#e0e6ed'; ctx.font = '11px Inter, sans-serif'; ctx.fillText('Box Plot', x, y - 5); ctx.fillText('Q1', q1X - 8, center + boxHeight/2 + 15); ctx.fillText('Q2', medX - 8, center + boxHeight/2 + 15); ctx.fillText('Q3', q3X - 8, center + boxHeight/2 + 15); } function drawScatterPlot(dataX, dataY, x, y, w, h) { const statsX = calculateStats(dataX); const statsY = calculateStats(dataY); // Calculate correlation const meanX = statsX.mean; const meanY = statsY.mean; const n = dataX.length; const num = dataX.reduce((sum, xv, i) => sum + (xv - meanX) * (dataY[i] - meanY), 0); const denX = Math.sqrt(dataX.reduce((sum, xv) => sum + (xv - meanX) ** 2, 0)); const denY = Math.sqrt(dataY.reduce((sum, yv) => sum + (yv - meanY) ** 2, 0)); const correlation = num / (denX * denY); // Draw points dataX.forEach((xv, i) => { const px = x + ((xv - statsX.min) / (statsX.max - statsX.min)) * w; const py = y + h - ((dataY[i] - statsY.min) / (statsY.max - statsY.min)) * h; ctx.fillStyle = '#00d4ff'; ctx.beginPath(); ctx.arc(px, py, 4, 0, Math.PI * 2); ctx.fill(); }); // Draw trend line (simplified) const slope = correlation * (statsY.std / statsX.std); const intercept = meanY - slope * meanX; const x1 = statsX.min; const y1 = slope * x1 + intercept; const x2 = statsX.max; const y2 = slope * x2 + intercept; const px1 = x + ((x1 - statsX.min) / (statsX.max - statsX.min)) * w; const py1 = y + h - ((y1 - statsY.min) / (statsY.max - statsY.min)) * h; const px2 = x + ((x2 - statsX.min) / (statsX.max - statsX.min)) * w; const py2 = y + h - ((y2 - statsY.min) / (statsY.max - statsY.min)) * h; ctx.strokeStyle = '#ff6b35'; ctx.lineWidth = 2; ctx.beginPath(); ctx.moveTo(px1, py1); ctx.lineTo(px2, py2); ctx.stroke(); // Labels ctx.fillStyle = '#e0e6ed'; ctx.font = '11px Inter, sans-serif'; ctx.fillText('Scatter Plot', x, y - 5); ctx.fillStyle = '#00ffff'; ctx.fillText(`r = ${correlation.toFixed(3)}`, x + w - 60, y + 15); } function drawCorrelationMatrix() { clearCanvas(ctx); ctx.fillStyle = '#e0e6ed'; ctx.font = '16px Inter, sans-serif'; ctx.fillText('Correlation Matrix', 300, 30); const features = ['Age', 'Income', 'Credit Score']; const corrMatrix = [ [1.00, 0.85, 0.75], [0.85, 1.00, 0.68], [0.75, 0.68, 1.00] ]; const cellSize = 120; const startX = 150; const startY = 80; // Draw labels ctx.font = '12px Inter, sans-serif'; features.forEach((f, i) => { ctx.fillStyle = '#e0e6ed'; ctx.fillText(f, startX + i * cellSize + 30, startY - 10); ctx.fillText(f, startX - 80, startY + i * cellSize + 65); }); // Draw cells corrMatrix.forEach((row, i) => { row.forEach((val, j) => { const x = startX + j * cellSize; const y = startY + i * cellSize; // Color based on correlation strength const intensity = Math.abs(val); const color = val > 0 ? `rgba(0, 212, 255, ${intensity})` : `rgba(255, 107, 53, ${intensity})`; ctx.fillStyle = color; ctx.fillRect(x, y, cellSize - 5, cellSize - 5); // Value ctx.fillStyle = intensity > 0.5 ? '#000' : '#e0e6ed'; ctx.font = 'bold 16px Inter, sans-serif'; ctx.textAlign = 'center'; ctx.fillText(val.toFixed(2), x + cellSize/2, y + cellSize/2 + 5); ctx.textAlign = 'left'; }); }); // Legend ctx.font = '12px Inter, sans-serif'; ctx.fillStyle = '#e0e6ed'; ctx.fillText('Strong Positive: r > 0.7', 200, 450); ctx.fillText('Moderate: r = 0.5 to 0.7', 380, 450); } function draw() { if (showingCorrelation) { drawCorrelationMatrix(); return; } const feature = featureSelect.value; const data = edaData[feature]; const stats = calculateStats(data); clearCanvas(ctx); // Title ctx.fillStyle = '#e0e6ed'; ctx.font = '16px Inter, sans-serif'; ctx.fillText('EDA Dashboard', 320, 25); // Left panel: Histogram drawHistogram(data, stats, 40, 60, 350, 180); // Top right: Box Plot drawBoxPlot(data, stats, 420, 70, 350, 80); // Bottom right: Scatter plot (feature vs next feature) const nextFeature = feature === 'age' ? 'income' : feature === 'income' ? 'credit' : 'age'; drawScatterPlot(data, edaData[nextFeature], 420, 180, 350, 150); // Statistics display ctx.fillStyle = '#e0e6ed'; ctx.font = '12px Inter, sans-serif'; ctx.fillText('Descriptive Statistics:', 40, 270); ctx.fillStyle = '#00d4ff'; ctx.fillText(`Mean: ${stats.mean.toFixed(2)}`, 40, 290); ctx.fillText(`Median: ${stats.median.toFixed(2)}`, 40, 310); ctx.fillText(`Std Dev: ${stats.std.toFixed(2)}`, 40, 330); ctx.fillText(`IQR: ${stats.iqr.toFixed(2)}`, 40, 350); ctx.fillStyle = '#e0e6ed'; ctx.fillText('Inferential Statistics:', 200, 270); ctx.fillStyle = '#00ffff'; const confidence = Number(confidenceSlider.value); const tCritical = confidence === 95 ? 1.96 : confidence === 99 ? 2.58 : 1.645; const marginError = tCritical * (stats.std / Math.sqrt(data.length)); ctx.fillText(`Confidence: ${confidence}%`, 200, 290); ctx.fillText(`CI: [${(stats.mean - marginError).toFixed(1)}, ${(stats.mean + marginError).toFixed(1)}]`, 200, 310); ctx.fillText(`P-value: 0.003 (significant)`, 200, 330); ctx.fillText(`Effect size (Cohen's d): 0.82`, 200, 350); // Legend ctx.strokeStyle = '#00ffff'; ctx.lineWidth = 2; ctx.beginPath(); ctx.moveTo(40, 390); ctx.lineTo(70, 390); ctx.stroke(); ctx.fillStyle = '#e0e6ed'; ctx.font = '11px Inter, sans-serif'; ctx.fillText('Mean', 75, 394); ctx.strokeStyle = '#ff6b35'; ctx.beginPath(); ctx.moveTo(130, 390); ctx.lineTo(160, 390); ctx.stroke(); ctx.fillText('Median', 165, 394); ctx.fillStyle = '#a8b2c1'; ctx.fillText('Click "Show Correlation Matrix" to see feature relationships', 40, 470); } if (confidenceSlider) { confidenceSlider.addEventListener('input', () => { confidenceSpan.textContent = confidenceSlider.value; draw(); }); } if (featureSelect) { featureSelect.addEventListener('change', () => { showingCorrelation = false; draw(); }); } function drawHistogram2() { showingCorrelation = false; const data = edaData[featureSelect.value]; const stats = calculateStats(data); clearCanvas(ctx); ctx.fillStyle = '#00d4ff'; ctx.font = '16px Inter, sans-serif'; ctx.fillText(`Histogram: Mean: ${stats.mean.toFixed(2)}, Median: ${stats.median.toFixed(2)}, Std: ${stats.std.toFixed(2)}`, 40, 30); drawHistogram(data, stats, 100, 80, 600, 300); } function drawBoxPlot2() { showingCorrelation = false; const data = edaData[featureSelect.value]; const stats = calculateStats(data); clearCanvas(ctx); ctx.fillStyle = '#00d4ff'; ctx.font = '16px Inter, sans-serif'; ctx.fillText(`Box Plot: Q1: ${stats.q1.toFixed(2)}, Q2: ${stats.median.toFixed(2)}, Q3: ${stats.q3.toFixed(2)}, IQR: ${stats.iqr.toFixed(2)}`, 40, 30); drawBoxPlot(data, stats, 100, 150, 600, 200); } function drawCorrelation2() { showingCorrelation = true; const feature = featureSelect.value; const nextFeature = feature === 'age' ? 'income' : feature === 'income' ? 'credit' : 'age'; clearCanvas(ctx); ctx.fillStyle = '#00d4ff'; ctx.font = '16px Inter, sans-serif'; const dataX = edaData[feature]; const dataY = edaData[nextFeature]; const statsX = calculateStats(dataX); const statsY = calculateStats(dataY); const meanX = statsX.mean; const meanY = statsY.mean; const num = dataX.reduce((sum, xv, i) => sum + (xv - meanX) * (dataY[i] - meanY), 0); const denX = Math.sqrt(dataX.reduce((sum, xv) => sum + (xv - meanX) ** 2, 0)); const denY = Math.sqrt(dataY.reduce((sum, yv) => sum + (yv - meanY) ** 2, 0)); const corr = num / (denX * denY); ctx.fillText(`Correlation: r = ${corr.toFixed(2)} (strong positive)`, 40, 30); drawScatterPlot(dataX, dataY, 150, 80, 500, 350); } // Button event listeners const btnHistogram = document.getElementById('btn-histogram'); const btnBoxplot = document.getElementById('btn-boxplot'); const btnCorrelation = document.getElementById('btn-correlation'); if (btnHistogram) btnHistogram.addEventListener('click', drawHistogram2); if (btnBoxplot) btnBoxplot.addEventListener('click', drawBoxPlot2); if (btnCorrelation) btnCorrelation.addEventListener('click', drawCorrelation2); drawHistogram2(); })(); /***** ---------- 11. FEATURE TRANSFORMATION CANVAS ------------------- */ (function transformationVis() { const select = document.getElementById('transformMethod'); const c = document.getElementById('canvas-transformation'); if (!select || !c) return; const ctx = c.getContext('2d'); // Non-linear data (quadratic relationship) const xData = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]; const yData = xData.map(x => x * x + Math.random() * 5); // y ≈ x² function draw() { const method = select.value; clearCanvas(ctx); if (method === 'polynomial') { // Show scatter plot and polynomial features ctx.fillStyle = '#e0e6ed'; ctx.font = '14px Inter, sans-serif'; ctx.fillText('Original Data (non-linear)', 50, 30); ctx.fillText('Feature Count: 2 → 6 (with degree=2)', 50, 50); // Draw scatter const maxX = Math.max(...xData); const maxY = Math.max(...yData); xData.forEach((x, i) => { const px = 50 + (x / maxX) * 250; const py = 300 - (yData[i] / maxY) * 200; ctx.fillStyle = '#00d4ff'; ctx.beginPath(); ctx.arc(px, py, 4, 0, Math.PI * 2); ctx.fill(); }); // Show polynomial transformation ctx.fillStyle = '#e0e6ed'; ctx.font = '12px Inter, sans-serif'; ctx.fillText('Polynomial Features:', 380, 80); ctx.fillText('Original: [x₁, x₂]', 380, 110); ctx.fillText('Transformed:', 380, 140); ctx.fillText('[1, x₁, x₂, x₁², x₁x₂, x₂²]', 380, 160); ctx.fillStyle = '#00ffff'; ctx.fillText('Example: x=3, y=5', 380, 200); ctx.fillText('→ [1, 3, 5, 9, 15, 25]', 380, 220); } else if (method === 'binning') { // Show binning visualization ctx.fillStyle = '#e0e6ed'; ctx.font = '14px Inter, sans-serif'; ctx.fillText('Age Binning: Continuous → Categorical', 50, 30); const ages = [5, 12, 18, 25, 32, 45, 58, 67, 73, 89]; const bins = [ { label: 'Child (0-18)', color: '#ff6b35', range: [0, 18] }, { label: 'Young Adult (19-35)', color: '#00d4ff', range: [19, 35] }, { label: 'Middle Age (36-60)', color: '#00ffff', range: [36, 60] }, { label: 'Senior (61+)', color: '#ff6b35', range: [61, 100] } ]; // Draw age line ctx.strokeStyle = 'rgba(255,255,255,0.2)'; ctx.beginPath(); ctx.moveTo(50, 150); ctx.lineTo(650, 150); ctx.stroke(); // Draw points with bin colors ages.forEach((age) => { const x = 50 + (age / 100) * 600; const bin = bins.find(b => age >= b.range[0] && age <= b.range[1]); ctx.fillStyle = bin ? bin.color : '#888'; ctx.beginPath(); ctx.arc(x, 150, 6, 0, Math.PI * 2); ctx.fill(); // Label ctx.font = '10px Inter, sans-serif'; ctx.fillText(age, x - 5, 170); }); // Legend ctx.font = '12px Inter, sans-serif'; bins.forEach((bin, i) => { const y = 220 + i * 25; ctx.fillStyle = bin.color; ctx.fillRect(50, y, 15, 15); ctx.fillStyle = '#e0e6ed'; ctx.fillText(bin.label, 75, y + 12); }); } else if (method === 'log') { // Log transformation visualization const skewedData = [1, 2, 5, 10, 20, 50, 100, 200, 500, 1000]; const logData = skewedData.map(x => Math.log(1 + x)); ctx.fillStyle = '#e0e6ed'; ctx.font = '14px Inter, sans-serif'; ctx.fillText('Log Transform: Reduces Right Skew', 50, 30); // Before histogram ctx.fillText('Before (Right-skewed)', 80, 80); const maxBefore = Math.max(...skewedData); skewedData.forEach((val, i) => { const h = (val / maxBefore) * 150; const x = 50 + i * 30; const y = 250 - h; ctx.fillStyle = '#ff6b35'; ctx.fillRect(x, y, 20, h); }); // After histogram ctx.fillStyle = '#e0e6ed'; ctx.fillText('After (More Normal)', 420, 80); const maxAfter = Math.max(...logData); logData.forEach((val, i) => { const h = (val / maxAfter) * 150; const x = 390 + i * 30; const y = 250 - h; ctx.fillStyle = '#00d4ff'; ctx.fillRect(x, y, 20, h); }); // Formula ctx.fillStyle = '#00ffff'; ctx.font = '12px Inter, sans-serif'; ctx.fillText('Formula: log(1 + x)', 280, 310); } } // Button event listeners const btnPolynomial = document.getElementById('btn-polynomial'); const btnBinning = document.getElementById('btn-binning'); const btnLog = document.getElementById('btn-log'); if (btnPolynomial) btnPolynomial.addEventListener('click', () => draw('polynomial')); if (btnBinning) btnBinning.addEventListener('click', () => draw('binning')); if (btnLog) btnLog.addEventListener('click', () => draw('log')); draw('polynomial'); })(); /***** ---------- 12. FEATURE CREATION CANVAS ------------------------- */ (function creationVis() { const select = document.getElementById('creationMethod'); const c = document.getElementById('canvas-creation'); if (!select || !c) return; const ctx = c.getContext('2d'); // Sample data const height = [1.6, 1.7, 1.8, 1.75, 1.65, 1.55, 1.9, 1.72]; // meters const weight = [60, 70, 80, 75, 65, 55, 95, 73]; // kg function createFeature(method) { if (method === 'multiply') { return height.map((h, i) => h * weight[i]); } else if (method === 'divide') { return weight.map((w, i) => w / height[i]); } else if (method === 'add') { return height.map((h, i) => h + weight[i]); } else if (method === 'bmi') { return weight.map((w, i) => w / (height[i] * height[i])); } return []; } function correlation(arr1, arr2) { const mean1 = arr1.reduce((a, b) => a + b) / arr1.length; const mean2 = arr2.reduce((a, b) => a + b) / arr2.length; const num = arr1.reduce((sum, v, i) => sum + (v - mean1) * (arr2[i] - mean2), 0); const den1 = Math.sqrt(arr1.reduce((sum, v) => sum + (v - mean1) ** 2, 0)); const den2 = Math.sqrt(arr2.reduce((sum, v) => sum + (v - mean2) ** 2, 0)); return num / (den1 * den2); } function draw() { const method = select.value; const newFeature = createFeature(method); clearCanvas(ctx); // Title ctx.fillStyle = '#e0e6ed'; ctx.font = '14px Inter, sans-serif'; ctx.fillText('Original Features', 80, 30); ctx.fillText('Created Feature', 430, 30); // Left: Scatter plot of height vs weight const maxH = Math.max(...height); const maxW = Math.max(...weight); ctx.strokeStyle = 'rgba(255,255,255,0.2)'; ctx.strokeRect(40, 50, 250, 200); ctx.font = '11px Inter, sans-serif'; ctx.fillStyle = '#a8b2c1'; ctx.fillText('Height (m)', 120, 265); ctx.save(); ctx.translate(25, 150); ctx.rotate(-Math.PI / 2); ctx.fillText('Weight (kg)', -30, 0); ctx.restore(); height.forEach((h, i) => { const x = 40 + (h / maxH) * 250; const y = 250 - (weight[i] / maxW) * 200; ctx.fillStyle = '#00d4ff'; ctx.beginPath(); ctx.arc(x, y, 5, 0, Math.PI * 2); ctx.fill(); }); // Right: Bar chart of new feature const maxNew = Math.max(...newFeature); const barW = 30; newFeature.forEach((val, i) => { const x = 380 + i * (barW + 5); const h = (val / maxNew) * 180; const y = 250 - h; ctx.fillStyle = '#00ffff'; ctx.fillRect(x, y, barW, h); }); // Formula and stats ctx.fillStyle = '#e0e6ed'; ctx.font = '12px Inter, sans-serif'; let formula = ''; if (method === 'multiply') formula = 'height × weight'; else if (method === 'divide') formula = 'weight / height'; else if (method === 'add') formula = 'height + weight'; else if (method === 'bmi') formula = 'weight / height²'; ctx.fillText(`Formula: ${formula}`, 50, 300); // Correlation const corr = correlation(newFeature, weight).toFixed(3); ctx.fillStyle = '#00ffff'; ctx.fillText(`Correlation with weight: ${corr}`, 50, 320); } // Button event listeners const btnInteraction = document.getElementById('btn-interaction'); const btnRatio = document.getElementById('btn-ratio'); const btnBmi = document.getElementById('btn-bmi'); if (btnInteraction) btnInteraction.addEventListener('click', () => draw('multiply')); if (btnRatio) btnRatio.addEventListener('click', () => draw('divide')); if (btnBmi) btnBmi.addEventListener('click', () => draw('bmi')); draw('bmi'); })(); /***** ---------- 13. DIMENSIONALITY REDUCTION (PCA) CANVAS ----------- */ (function pcaVis() { const slider = document.getElementById('slider-components'); const pcaValSpan = document.getElementById('pcaValue'); const btn = document.getElementById('btn-pca-apply'); const c = document.getElementById('canvas-pca'); if (!slider || !c || !btn) return; const ctx = c.getContext('2d'); // 3D data (simplified for visualization) const data3D = [ [2.5, 2.4, 1.0], [0.5, 0.7, 0.2], [2.2, 2.9, 1.1], [1.9, 2.2, 0.9], [3.1, 3.0, 1.2], [2.3, 2.7, 1.0], [2.0, 1.6, 0.6], [1.0, 1.1, 0.4], [1.5, 1.6, 0.6], [1.1, 0.9, 0.3] ]; // Simplified PCA (not real eigenvalue computation, just for viz) const explainedVariance = [0.72, 0.23, 0.05]; // PC1, PC2, PC3 function draw() { const nComponents = Number(slider.value); pcaValSpan.textContent = nComponents; clearCanvas(ctx); // Title ctx.fillStyle = '#e0e6ed'; ctx.font = '14px Inter, sans-serif'; ctx.fillText('PCA: Dimensionality Reduction', 250, 25); // Original 3D visualization (pseudo-3D) ctx.fillText('Original Data (3D)', 80, 60); ctx.strokeStyle = 'rgba(255,255,255,0.15)'; ctx.strokeRect(30, 70, 280, 150); data3D.forEach(point => { const x = 50 + point[0] * 60; const y = 150 - point[1] * 30; const size = 2 + point[2] * 3; ctx.fillStyle = '#00d4ff'; ctx.beginPath(); ctx.arc(x, y, size, 0, Math.PI * 2); ctx.fill(); }); ctx.fillStyle = '#a8b2c1'; ctx.font = '11px Inter, sans-serif'; ctx.fillText('Features: 3', 50, 235); // Transformed data (2D projection) ctx.fillStyle = '#e0e6ed'; ctx.font = '14px Inter, sans-serif'; ctx.fillText(`Reduced Data (${nComponents}D)`, 420, 60); ctx.strokeStyle = 'rgba(255,255,255,0.15)'; ctx.strokeRect(360, 70, 280, 150); // Project to PC space (simplified) data3D.forEach(point => { const pc1 = point[0] * 0.7 + point[1] * 0.3; const pc2 = point[0] * 0.3 + point[1] * 0.7; const x = 390 + pc1 * 60; const y = 150 - pc2 * 30; ctx.fillStyle = '#00ffff'; ctx.beginPath(); ctx.arc(x, y, 4, 0, Math.PI * 2); ctx.fill(); }); ctx.fillStyle = '#a8b2c1'; ctx.font = '11px Inter, sans-serif'; ctx.fillText(`Features: ${nComponents}`, 380, 235); // Explained variance bar chart ctx.fillStyle = '#e0e6ed'; ctx.font = '14px Inter, sans-serif'; ctx.fillText('Explained Variance per Component', 180, 270); const barWidth = 80; const maxH = 80; explainedVariance.slice(0, nComponents).forEach((variance, i) => { const x = 120 + i * (barWidth + 20); const h = variance * maxH; const y = 360 - h; // Bar const colors = ['#00d4ff', '#00ffff', '#ff6b35']; ctx.fillStyle = colors[i]; ctx.fillRect(x, y, barWidth, h); // Label ctx.fillStyle = '#e0e6ed'; ctx.font = '12px Inter, sans-serif'; ctx.fillText(`PC${i + 1}`, x + 25, 375); ctx.fillText(`${(variance * 100).toFixed(1)}%`, x + 15, y - 5); }); // Cumulative variance const cumulative = explainedVariance.slice(0, nComponents).reduce((a, b) => a + b, 0); ctx.fillStyle = '#00ffff'; ctx.font = '13px Inter, sans-serif'; ctx.fillText(`Cumulative Variance: ${(cumulative * 100).toFixed(1)}%`, 450, 310); ctx.fillText(`Dimensionality Reduction: 3 → ${nComponents}`, 450, 330); } slider.addEventListener('input', draw); btn.addEventListener('click', draw); draw(); })();